A nomogram combining neutrophil to lymphocyte ratio (NLR) and prognostic nutritional index (PNI) to predict distant metastasis in gastric cancer

In this study, We aim to explore the association between the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), lymphocyte to monocyte ratio (LMR) and prognostic nutritional index (PNI) and distant metastasis of gastric cancer and develop an efficient nomogram for screening patients with distant metastasis. A total of 1281 inpatients with gastric cancer were enrolled and divided into the training and validation set.Univariate, Lasso regression and Multivariate Logistic Regression Analysis was used to identify the risk factors of distant metastasis. The independent predictive factors were then enrolled in the nomogram model. The nomogram’s predictive perform and clinical practicality was evaluated by receiver operating characteristics (ROC) curves, calibration curves and decision curve analysis. Multivariate Logistic Regression Analysis identified d-dimer, CA199, CA125, NLR and PNI as independent predictive factors. The area under the curve of our nomogram based on these factors was 0.838 in the training cohort and 0.811 in the validation cohort. The calibration plots and decision curves demonstrated the nomogram’s good predictive performance and clinical practicality in both training and validation cohort. Therefore,our nomogram could be an important tool for clinicians in screening gastric cancer patients with distant metastasis.


Validation and clinical use of nomogram
The AUC values of our nomogram for predicting distant metastasis were 0.838 in the training set (Fig. 4A) and 0.811 in the validation set (Fig. 4B).The calibration curve of our nomogram for the probability of distant metastasis showed good consistency between prediction and observation in both training (Fig. 4C) and validation cohort (Fig. 4D).Decision curva analysis (DCA) demonstrated that our nomogram conferred a positive net benefit compared to the all-or-none scheme at a threshold probability ranging from 10 to 95% in both training set (Fig. 5A) and testing set (Fig. 5B).

Discussion
Distant metastasis, the spread of tumor cells from the primary site to distant organs, is often associated with poor prognosis in various cancers.Patients diagnosed with distant metastasis in gastric cancer typically experience significantly lower five-year survival rates compared to those with localized disease 3 .Certainly, the treatment of stomach cancer with distant metastasis is still one of the important challenges faced by clinicians.Accurate prediction of distant metastasis prior to treatment is crucial for patients to avoid unnecessary surgical operations and develop the optimal treatment regimen.PET/CT plays an indispensable role in screening distant metastasis in gastric cancer and has high specificity for detection, but low sensitivity 27 .
The clinical application of liquid biopsy is one of the gastric cancer research hotspots.It has been reported that application of liquid biopsy is feasible for gastric cancer staging.Zeng et al found that folate receptor-positive circulating tumor cells (FR+ CTC ) levels correlated with advanced clinical stage and could effectively predict peritoneal metastasis (PM) in gastric cancer 28 .The data of Pu et al documented that levels of ccf-DNA were elevated in late-stage cancers 29 .Despite demonstrating promising applications, liquid biopsy technology is still in the exploratory phase.The lack of large-scale clinical study validation, standardized operational procedures and data processing methods, and prohibitive costs prevent its widely application in the clinic 30 .
In this study, we explored the association between peripheral blood biomarkers related to inflammation, nutrition, coagulation and tumor markers with distant metastasis in gastric cancer.And we identified that d-dimer, CA125, CA199, NLR and PNI were significantly associated with distant metastasis in GC.
Tumor markers play an important role in predicting the stage of gastric cancer.Nakata et al discovered that CA125 performed better than imaging modalities including computed tomography and ultrasonography in predicting peritoneal dissemination 31 .Li et al found that the positive levels of CA125 in patients with distant metastasis were statistically significant compared to those without distant metastasis and healthy control group while not statistically significant between patients without distant metastasis and healthy control group, which means CA125 related to the distant metastasis of GC 32 .Kochi et al indicated that the positivity rates of CA199 were increased significantly in stage IV than stage III or below (more than 50% vs less than 30%) 33 .These results provide support for our conclusion.
The association between blood coagulation and cancer development is well recognized 34,35 .d-dimer is a soluble fibrin degradation product (FDP) composed of two cross-linked D fragments of the fibrin protein and has been used as a screening and diagnostic tool in numerous coagulopathies and thrombotic disease.In patients with gastric cancer, increasing d-dimer level is associated with advanced clinical pathological stage, more lymph node metastasis, distant metastasis and poor overall survival (OS) [36][37][38] .
It is estimated that 15-40% cancer patients with malnutrition at diagnosis and 40-80% cases will be malnourished during the treatment of the disease 39 .Malnutrition worsens OS and increase the postoperative complications in cancer patients [40][41][42] .Cachexia is not an inevitable consequence of cancer, but it is clearly associated with advanced-stage disease 43 .PNI is a simple and effective indictor for assessing nutrition status.Low PNI not only predicts poor survival in cancer patients, but also associated with TNM stage 26,44 .
The chronic and sustained inflammation induced by tumors leads to changes in hematopoiesis and in the systemic composition and functional status of immune cells, thereby promotes metastasis 45 .Studies have reported that systemic inflammation indexes have good predictive and prognostic value in patients with tumors.High levels of NLR were associated with distant metastasis and poor prognosis in gastric cancer [46][47][48][49] .
The predictive nomogram based on these factors performed excellent predictive power in both training set and validation.The DCA curves showed that the nomogram had good clinical effectiveness.More significantly, the indicators used in our nomogram were affordable and easily available.Admittedly, there are still some limitations of our study.Firstly, the results of our study require further validation due to the limited sample size and the absence of external data validation.Secondly, the cut-off value of NLR and PNI is still controversial, which means the threshold value in our study may not be applicable to other researches.Thirdly, our research was based on inpatients, the applicability for outpatients needs further exploration.Therefore, further large-scale multicenter prospective studies are necessary to validate the results of our research.

Conclusion
In conclusion, our study indicated that d-dimer, CA199, CA125, NLR and PNI were independent predictive factors for distant metastasis and developed a nomogram based on these factors.The nomogram performed well in predicting distant metastasis in gastric cancer patients, which means it can be an important screen tool for clinicians and help to provide individualized treatment strategies.

Patients
From January 2018 to April 2023, a total of 1454 inpatients with gastric cancer from Xiangya Hospital were enrolled in this study.Patient's eligibility criteria for this study are as follows: (1) all patient's pathology confirmed as adenocarcinoma; (2) no prior treatment with neoadjuvant chemotherapy or surgery before obtaining their first peripheral blood data; (3) not gastric remnant carcinoma.Patients were excluded if they met any of the following criteria: (1) lack of pre-treatment laboratory data; (2) discover thrombosis or hematemesis in the past 3 months (3) uncertainty about the presence or absence of distal spread; (4) with active inflammatory, chronic infection, or autoimmune rheumatic diseases; (5) with other malignancies or gastrointestinal stromal tumor (GIST).The flowchart for the screening process of eligible gastric cancer patients is presented in Fig. 1A.Ultimately, a total of 1281 patients with gastric cancer were screened.896 patients were assigned to the training cohort, while other 385 patients for validation cohort.

Data collection and processing
The collection of clinical parameters included basic demographic information (age, sex), hematological parameters (White blood cell count, Red blood cell count, Neutrophil count, Lymphocyte count, Monocyte count, Platelet count, Hemoglobin, Total protein, Albumin, d-dimer), and tumor markers (CEA, CA125, CA199).All laboratory blood test data were collected from tests performed on the patients' first admission prior to any treatment.Distant metastasis was classified according to the 8th AJCC tumor classification and obtained from the hospital medical records.And five composite inflammatory and nutritional markers (NLR, PLR, SII, LMR and Vol:.( 1234567890

Development and validation of the nomogram
Lasso regression and multivariate Logistic regression were used to select independent predictive factors from the training cohort.Subsequently, a nomogram was constructed using the independent factors identified through multivariate analysis.Area under the ROC curves (AUC) and calibration curves were utilized to assess the predictive ability of the nomogram.Additionally, the decision curve analysis (DCA) was used to evaluated the clinical utility of the nomogram by quantifying the net benefits.

Statistical analysis
The statistical analysis was performed with R studio (version 4.3.1).Continuous variables were presented as mean and standard deviation, and categorical variables were presented as numbers and percentages.The p-value < 0.05 was considered statistically significant.Univariate analysis in Tables 1 and 2 were performed by the "autoReg" package.The "glmnet" package was utilized to perform Lasso binary logistic regression.And the "rms" package was employed to perform multivariate binary logistic regression, visualization of nomogram and plot calibration curve.ROC curves for assessing the discriminatory power of the nomogram and identifying the optimal cutoff values was done with pROC package.And decision curve analysis was performed with the "rmda" package.

Ethical approval and consent to participate
The retrospective design of the study received approval from the Ethics Committee of Xiangya Hospital (approval no.20200237).The procedures used in this study adhere to the tenets of the Declaration of Helsinki.As this was a retrospective observational study, informed consent was waived by the Ethics Committee of Xiangya Hospital.
https://doi.org/10.1038/s41598-024-65307-7PNI) were obtained from hematological indexes.Figure1Cpresented the calculation method of each composite indexes.The established upper normal limits for CEA, CA199 and CA125, were 5 ng/mL, 35 U/mL and 35 U/ mL.Receiver operating curve (ROC) was used to determine the optimal cut-off values for composite inflammatory and nutritional markers by calculating the maximal Youden index as shown in Fig.1B.Tumor markers and composite markers were divided into two groups based on their thresholds or cutoff values.Hypoalbuminemia was defined as Albumin < 35 g/L.Total protein and d-dimer levels were divided into two groups based on reference values, and Albumin divided into two groups based on the presence or absence of hypoproteinemia.

Figure 1 .
Figure 1.(A) Flowchart of patient selection process in the study; (B) ROC curves of the composite inflammatory and nutritional markers for predicting distant metastasis in patients with gastric cancer; (C) calculation methods for the composite inflammatory and nutritional markers.

Figure 2 .
Figure 2. Using Lasso regression to screen potential variables: (A) LASSO coefficient profiles of 17 variables; (B) Ten-fold cross validation for tuning parameter selection in the LASSO Logistic regression model, the vertical dashed lines represent the optimal values determined by the minimum criteria and 1 − standard error (S.E.) criteria.

Figure 3 .
Figure 3. Nomogram for predicting distant metastasis risk in gastric cancer patients.

Figure 4 .
Figure 4. ROC curves and calibration curves of nomogram for predicting distant metastasis in patients with gastric cancer.(A) ROC curve of the nomogram in the training cohort; (B) ROC curve of the nomogram in the validation cohort; (C) the calibration curve of nomogram in the training cohorts; (D) the calibration curve of the nomogram in the validation cohorts.

Figure 5 .
Figure 5. Decision curve analysis of the nomogram for the prediction of distant metastasis in gastric cancer patients.(A) Training cohort; (B) validation cohort.

Table 1 .
Baseline clinical characteristics associated with distant metastasis in gastric cancer patients.M1: patients with distant metastasis; M0: patients without distant metastasis; lymph nods: distant lymph node metastasis beyond regional lymph nodes; multiple: two or more distant metastasis sites.

Table 2 .
Baseline clinical characteristics of patients in training set and validation set and univariate analysis in training set.M1: patients with distant metastasis; M0: patients without distant metastasis.